{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-08-12T13:32:42.007137945Z",
     "start_time": "2023-08-12T13:32:41.207235323Z"
    }
   },
   "outputs": [],
   "source": [
    "from MyEDFImports import edf_dir, import_ecg, import_stages, get_edf_filenames, load_all_data, load_data_one_file, three_stages_transform, stages_names_3_outputs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pywt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "filenames = get_edf_filenames()\n",
    "name = filenames[0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T13:37:07.638757482Z",
     "start_time": "2023-08-12T13:37:07.598028253Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "'CX230050.edf'"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-19T10:20:32.651032Z",
     "end_time": "2023-07-19T10:20:32.721846Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CN223100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CP229110.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CX230050.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/DG220020.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/DO223050.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/MyEDFImports.py:39: RuntimeWarning: Channel names are not unique, found duplicates for: {'CHIN EMG'}. Applying running numbers for duplicates.\n",
      "  raw = mne.io.read_raw_edf(path + \"//\" + name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/LA216100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/LM230010.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/TK221110.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/VC209100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/VP214110.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/WD224010.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/MyEDFImports.py:39: RuntimeWarning: Channel names are not unique, found duplicates for: {'CHIN EMG'}. Applying running numbers for duplicates.\n",
      "  raw = mne.io.read_raw_edf(path + \"//\" + name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<RawEDF | CN223100.edf, 1 x 15611000 (31222.0 s), ~6 kB, data not loaded> with 1561 windows\n",
      "<RawEDF | CP229110.edf, 1 x 20078000 (40156.0 s), ~6 kB, data not loaded> with 2007 windows\n",
      "<RawEDF | CX230050.edf, 1 x 17981000 (35962.0 s), ~6 kB, data not loaded> with 1798 windows\n",
      "<RawEDF | DG220020.edf, 1 x 17756000 (35512.0 s), ~6 kB, data not loaded> with 1775 windows\n",
      "<RawEDF | DO223050.edf, 1 x 18066500 (36133.0 s), ~6 kB, data not loaded> with 1806 windows\n",
      "<RawEDF | LA216100.edf, 1 x 16333500 (32667.0 s), ~6 kB, data not loaded> with 1633 windows\n",
      "<RawEDF | LM230010.edf, 1 x 17246500 (34493.0 s), ~6 kB, data not loaded> with 1724 windows\n",
      "<RawEDF | TK221110.edf, 1 x 15991000 (31982.0 s), ~6 kB, data not loaded> with 1599 windows\n",
      "<RawEDF | VC209100.edf, 1 x 18434500 (36869.0 s), ~6 kB, data not loaded> with 1843 windows\n",
      "<RawEDF | VP214110.edf, 1 x 17252500 (34505.0 s), ~6 kB, data not loaded> with 1725 windows\n",
      "<RawEDF | WD224010.edf, 1 x 17774000 (35548.0 s), ~6 kB, data not loaded> with 1777 windows\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([[ 5.03547723e-08,  5.03547723e-08,  5.03547723e-08, ...,\n        -3.16781872e-04, -3.34305333e-04, -3.39542229e-04],\n       [-3.33298238e-04, -3.35513848e-04, -3.39542229e-04, ...,\n         3.87731746e-06, -3.37376974e-06,  4.28015564e-06],\n       [ 5.03547723e-08, -2.56809339e-06, -2.97093156e-06, ...,\n        -3.15170520e-04, -2.89993133e-04, -2.88381781e-04],\n       ...,\n       [ 3.97299153e-05,  5.38292515e-05,  6.02746624e-05, ...,\n        -1.90492103e-04, -1.98750286e-04, -1.98750286e-04],\n       [-1.71357290e-04, -1.38324559e-04, -1.36310369e-04, ...,\n        -8.63584344e-05, -9.74364843e-05, -1.03076219e-04],\n       [-9.09910735e-05, -7.12520027e-05, -7.52803845e-05, ...,\n        -1.24225223e-04, -1.07104601e-04, -1.10730144e-04]])"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all_patients = load_all_data()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T13:41:26.815224874Z",
     "start_time": "2023-08-12T13:40:48.058659765Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7fbabb5cfaf0>]"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 1 Axes>",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.plot(data_all_patients.flatten()[:15610000])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T13:41:35.070448290Z",
     "start_time": "2023-08-12T13:41:26.815860987Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CN223100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "<Info | 7 non-empty values\n",
      " bads: []\n",
      " ch_names: ECG\n",
      " chs: 1 EEG\n",
      " custom_ref_applied: False\n",
      " highpass: 0.0 Hz\n",
      " lowpass: 250.0 Hz\n",
      " meas_date: 2007-10-23 23:02:43 UTC\n",
      " nchan: 1\n",
      " projs: []\n",
      " sfreq: 500.0 Hz\n",
      ">\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_one_patient = import_ecg(name)\n",
    "print(data_one_patient.info)\n",
    "y = data_one_patient[0][0].T\n",
    "x = data_one_patient[0][1]\n",
    "def plot_edf(x,y):\n",
    "    plt.figure(figsize=(20, 6))\n",
    "    plt.step(x, y, )\n",
    "    plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))\n",
    "    plt.legend(['ECG'])\n",
    "    plt.xlabel('time [s]')\n",
    "    plt.ylabel('voltage [V]')\n",
    "    plt.title(f\"ECG of {name}\")\n",
    "    plt.show()\n",
    "plot_edf(x,y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T15:37:47.695332190Z",
     "start_time": "2023-08-12T15:37:23.783376888Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "def stages_one_file(name, edf_dir = edf_dir):\n",
    "    stages_file = edf_dir + '//' + name + '_stages.txt'\n",
    "    stages = pd.read_csv(stages_file, comment='%', delimiter='\t', index_col=0).drop('Unnamed: 3', axis=1)\n",
    "    return stages"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T15:37:54.078829630Z",
     "start_time": "2023-08-12T15:37:54.068324085Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "stages = stages_one_file(name).drop(['Time'], axis=1)\n",
    "stages = stages['stage'].to_numpy()\n",
    "stages = three_stages_transform(stages)\n",
    "stages = np.array(stages)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T16:00:20.959224863Z",
     "start_time": "2023-08-12T16:00:20.917379870Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_stages = np.repeat(stages, int(1e4))\n",
    "# adding 1000 points at the end because the stages are only up to a \"whole\" point\n",
    "y_stages = np.append(y_stages, np.repeat(y_stages[-1], 1000))\n",
    "len(y_stages) == len(y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T15:38:27.978406785Z",
     "start_time": "2023-08-12T15:38:27.867996373Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_hypnogram(x, y, y_stages):\n",
    "    fig, ax1 = plt.subplots(figsize=(20,6))\n",
    "    ax2 = ax1.twinx()\n",
    "    ax1.step(x, y, 'b-')\n",
    "    ax2.step(x,y_stages, 'g-')\n",
    "    ax2.invert_yaxis()\n",
    "    ax1.set_xlabel('time [s]')\n",
    "    ax1.set_ylabel('Voltage [V]', color='b')\n",
    "    ax2.set_ylabel('Sleep Stage', color='g')\n",
    "    ax1.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))\n",
    "    # plt.legend(['ECG'])\n",
    "    plt.title(f\"ECG of {name}\")\n",
    "    plt.show()\n",
    "draw_hypnogram(x, y, y_stages)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T15:38:41.839061342Z",
     "start_time": "2023-08-12T15:38:36.425248105Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Remove parts where there are spikes (threshold 0.0026)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<RawEDF | CN223100.edf, 1 x 15611000 (31222.0 s), ~6 kB, data not loaded> with 1561 windows\n",
      "15230000 15231000\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (15230000,) and (15231000,)",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[69], line 10\u001B[0m\n\u001B[1;32m      8\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;28mlen\u001B[39m(filtered_y), \u001B[38;5;28mlen\u001B[39m(y_stages))\n\u001B[1;32m      9\u001B[0m t \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mlinspace(\u001B[38;5;241m0\u001B[39m,\u001B[38;5;28mlen\u001B[39m(filtered_y)\u001B[38;5;241m/\u001B[39m\u001B[38;5;241m500\u001B[39m, \u001B[38;5;28mlen\u001B[39m(filtered_y), endpoint\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m---> 10\u001B[0m \u001B[43mdraw_hypnogram\u001B[49m\u001B[43m(\u001B[49m\u001B[43mt\u001B[49m\u001B[43m,\u001B[49m\u001B[43mfiltered_y\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_stages\u001B[49m\u001B[43m)\u001B[49m\n",
      "Cell \u001B[0;32mIn[31], line 5\u001B[0m, in \u001B[0;36mdraw_hypnogram\u001B[0;34m(x, y, y_stages)\u001B[0m\n\u001B[1;32m      3\u001B[0m ax2 \u001B[38;5;241m=\u001B[39m ax1\u001B[38;5;241m.\u001B[39mtwinx()\n\u001B[1;32m      4\u001B[0m ax1\u001B[38;5;241m.\u001B[39mstep(x, y, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mb-\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m----> 5\u001B[0m \u001B[43max2\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstep\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43my_stages\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mg-\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m      6\u001B[0m ax2\u001B[38;5;241m.\u001B[39minvert_yaxis()\n\u001B[1;32m      7\u001B[0m ax1\u001B[38;5;241m.\u001B[39mset_xlabel(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtime [s]\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/axes/_axes.py:2176\u001B[0m, in \u001B[0;36mAxes.step\u001B[0;34m(self, x, y, where, data, *args, **kwargs)\u001B[0m\n\u001B[1;32m   2174\u001B[0m _api\u001B[38;5;241m.\u001B[39mcheck_in_list((\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpre\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpost\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmid\u001B[39m\u001B[38;5;124m'\u001B[39m), where\u001B[38;5;241m=\u001B[39mwhere)\n\u001B[1;32m   2175\u001B[0m kwargs[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdrawstyle\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124msteps-\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;241m+\u001B[39m where\n\u001B[0;32m-> 2176\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/axes/_axes.py:1688\u001B[0m, in \u001B[0;36mAxes.plot\u001B[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001B[0m\n\u001B[1;32m   1445\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m   1446\u001B[0m \u001B[38;5;124;03mPlot y versus x as lines and/or markers.\u001B[39;00m\n\u001B[1;32m   1447\u001B[0m \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m   1685\u001B[0m \u001B[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001B[39;00m\n\u001B[1;32m   1686\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m   1687\u001B[0m kwargs \u001B[38;5;241m=\u001B[39m cbook\u001B[38;5;241m.\u001B[39mnormalize_kwargs(kwargs, mlines\u001B[38;5;241m.\u001B[39mLine2D)\n\u001B[0;32m-> 1688\u001B[0m lines \u001B[38;5;241m=\u001B[39m [\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_lines(\u001B[38;5;241m*\u001B[39margs, data\u001B[38;5;241m=\u001B[39mdata, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)]\n\u001B[1;32m   1689\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines:\n\u001B[1;32m   1690\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39madd_line(line)\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/axes/_base.py:311\u001B[0m, in \u001B[0;36m_process_plot_var_args.__call__\u001B[0;34m(self, data, *args, **kwargs)\u001B[0m\n\u001B[1;32m    309\u001B[0m     this \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m args[\u001B[38;5;241m0\u001B[39m],\n\u001B[1;32m    310\u001B[0m     args \u001B[38;5;241m=\u001B[39m args[\u001B[38;5;241m1\u001B[39m:]\n\u001B[0;32m--> 311\u001B[0m \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_plot_args\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    312\u001B[0m \u001B[43m    \u001B[49m\u001B[43mthis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/axes/_base.py:504\u001B[0m, in \u001B[0;36m_process_plot_var_args._plot_args\u001B[0;34m(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001B[0m\n\u001B[1;32m    501\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxes\u001B[38;5;241m.\u001B[39myaxis\u001B[38;5;241m.\u001B[39mupdate_units(y)\n\u001B[1;32m    503\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m x\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m] \u001B[38;5;241m!=\u001B[39m y\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m]:\n\u001B[0;32m--> 504\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mx and y must have same first dimension, but \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    505\u001B[0m                      \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhave shapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m x\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m2\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m y\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m2\u001B[39m:\n\u001B[1;32m    507\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mx and y can be no greater than 2D, but have \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    508\u001B[0m                      \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mshapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "\u001B[0;31mValueError\u001B[0m: x and y must have same first dimension, but have shapes (15230000,) and (15231000,)"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from MyEDFImports import remove_ecg_artifacts\n",
    "y = load_data_one_file(data_one_patient)\n",
    "filtered_y, filtered_stages = remove_ecg_artifacts(y, stages)\n",
    "filtered_y = filtered_y.flatten()\n",
    "y_stages = np.repeat(filtered_stages, int(1e4))\n",
    "# adding 1000 points at the end because the stages are only up to a \"whole\" point\n",
    "y_stages = np.append(y_stages, np.repeat(y_stages[-1], 1000))\n",
    "print(len(filtered_y), len(y_stages))\n",
    "t = np.linspace(0,len(filtered_y)/500, len(filtered_y), endpoint=False)\n",
    "draw_hypnogram(t,filtered_y, y_stages)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T16:04:56.240507111Z",
     "start_time": "2023-08-12T16:04:32.993515248Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0, 0, 0, ..., 0, 0, 0])"
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_stages"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T16:03:58.413835820Z",
     "start_time": "2023-08-12T16:03:58.384083003Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.0000000e+00, 2.0000000e-03, 4.0000000e-03, ..., 3.0459994e+04,\n       3.0459996e+04, 3.0459998e+04])"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,len(filtered_y)/500, len(filtered_y), endpoint=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T16:02:41.030781256Z",
     "start_time": "2023-08-12T16:02:40.917111765Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.000e+00, 2.000e-03, 4.000e-03, ..., 3.040e+00, 3.042e+00,\n       3.044e+00])"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,len(y)/500, len(y), endpoint=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T15:45:41.620366184Z",
     "start_time": "2023-08-12T15:45:41.572290053Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "1523"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-12T15:43:10.381144446Z",
     "start_time": "2023-08-12T15:43:10.374248199Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x432 with 2 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stages_6 = stages_one_file(name)\n",
    "stages_6 = stages_6['stage'].to_numpy()\n",
    "stages_6 = np.repeat(stages_6, int(1e4))\n",
    "# adding 1000 points at the end because the stages are only up to a \"whole\" point\n",
    "stages_6 = np.append(stages_6, np.repeat(stages_6[-1], 1000))\n",
    "len(y_stages)\n",
    "draw_hypnogram(x, y, stages_6)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-19T10:21:48.494007Z",
     "end_time": "2023-07-19T10:22:12.978602Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x117ade6d780>]"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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1Vn4oPVZh2+5kN5bVQ2+tZ9X2Uh6cu86p8Hh95Q7GPLKIt1ZHbuw8Yd0NHjrsQ+jw7rsPlfHKsm2Uhtz3crisgv/+61LH4swkLy71T/ugneDmWFlVTUVVNX96f2PNZweOllNWUVUrya3dcfL2MK1i8pGSIyeY9dFmbhndh6ws/7YEh978lwoHj5VTtPkAo/t1SulyE1VWUcXA+97mmmGn8a3BXRiY36bms/tfX8v/G3Yan351gFGnd8JgeH7JV47HENy5N+w+wriz7MvM/jRwh/m8NbWrNrYfPM6Dcz+vGTLeC2l4AeH7hyLd9s8VtZ73Eurs++fXvG6Zm8PhFHSd1wRRT+N+H+ijf2GfPAoL2nkcTXL+9ek2Hnu3mE17j7LxwfFk2yS8yqpqfjNvPT+8qCdtmzfmcFkFFz/8Pk98dzDnhPz9NzxbxNLNB/j0rkto17yxp9UQkarXDpVVsHBdYHiDv32yhb99sqXW2de/V+zgucWBhDCdxBss463W+6N1dviFTZfeqmpDWUVVxJ511z2zNOKIvvFw4neJNTR1RVU1jbKzuO/fa1mwbjdb9h2ja7umyS84CXZD56ejVCQH0ASRtEx45PP/vnTyjKXnHXN55NsD+ebZ+bXKzF+7mz8v2sSGPUcYe2YnyiurKTl8gt/OW8+LPzi3ptxma3TO8IEOvbjGCs1z188q4qaRPTm7W1vbQdhCD5iHUjQSbnCZ763bwz+LtvLRxn2c16sDOw8e52Gbh0QFXf2X+Aa4SzgeB68JivccYfTv3uf+iWcyM6Sjwdb9xx1bRn0c1RtWE6IJIknpeJ9B0IGj5VxoM/Dgy8u2MfqMTrRs0giAeWt21Ty86N11e3h33cnBxRZ/Gb3hLtpBpyKFo62+8/lulm89wEshycwvDp+orOkaPfuz8FFq3OFUMjDG8MR7GzmnoB2NsoVG2Vlc9n8f1nx+12trHFlOOknldu02TRAJiLc7XDqoqKquVacZ6j/F++h/79s889/n0KVNU37wt+jDEW/Zd5R2zRvXJJSg385bX1N3bpdI//LBproTHRS+zL1Hyrn44fddXSbA8Yoq/vT+Rm64oIdtdV0meX3lTs/GRqoPN6s8Pyrey/BeHeg9/U33FpJimiCStG7X4Vp18H5VWVVNlghZWcKbq3byowhdNEN9769LGZDfOma5i37zHn07tWTeTy+smXbzC8tjjl5b6vKIs052c03E79/5gvfWl5DXIpcrhuRTVW3Yf7ScvJaJ3+zmdz95/jOvQ0iIm2Mdfecp2ycSpIz2YvKhu15dzV2v+u/Oy3C9pr/Jeb3a89z1wxJ6FkW8XU/X7z5c0y4B1EkOJ3z2rF03BZ/m99SHX9KxVS7XPL2k1ucPXzWQR9/d4EVoNV77bAdTRvSqNc3nHXwiKq+sZtZHm3nmo8188+wudGyVy5VD8jlwrIImOVmMeWSRp8N1ACzbsp/V2w9xzbDTKD1eQbPcbHJznL3RVm+UU0n5T/E+fv7ySna41JOjz52RL63nr93Nw29/wbizTuFbg7twWrvm7E5xF9xUKdoSeDDU5zsP1UkOALdG6MaYSsE71ssrq9lVWsaSzftZEeGemZLDJ2jbrBE52f68bSp0u3tsYTEAd/uo7ePRBRv4ndXpIDhMixv0CkIlbM+hMg6EnD296NFAgq9b/fWfXLSJJxfVv+1h3a5DHCmrDHTzMzDy9I41nx0vr+KMu9/i4tM7MvN759RqTFe1VVYbrp+1NOrTzPYcKmPog/aPwFXx+12UHmlOcmPUBE0QGe6FpVt5wed3jyYieP9J0FldWvH987rz2MLimru1g4lhmXUmr+xFSw59pr+pz/RWmiBUelu9/VCt+ziCMqnHmRc0OSjQsZiUUkpFoAlCKaWULU0QSimlbGmCUEopZUsThFJKKVuaIJRSStlyJEGIyDgRWS8ixSIy1ebzXBF50fp8sYgUhHw2zZq+XkTGhkyfKSJ7RMT/41gopZTH3LiTOukEISLZwOPApUA/4GoR6RdW7DrggDGmF/AI8JD13X7AJOBMYBzwhDU/gGesaUoppWJwYywmJ64ghgLFxphNxphy4AVgYliZicAs6/XLwCgJDLU5EXjBGHPCGPMlUGzND2PMIqDhPSVcKaV8wokE0QUIHcthmzXNtowxphIoBdrH+d2oRORGESkSkaKSkpIEQ1dKqczgyyomrxljnjTGFBpjCvPy8rwORymlMoYTCWI70DXkfb41zbaMiOQArYF9cX5XKaWUB5xIEEuB3iLSXUQaE2h0nhNWZg4w2Xp9JfCuMcZY0ydZvZy6A72BugPoK6WUSrmkE4TVpvBjYB7wOfCSMWaNiNwnIt+wij0NtBeRYuB/ganWd9cALwFrgbeAm4wxVQAi8jzwMdBXRLaJyHXJxqqUUip+jgz3bYyZC8wNm3Z3yOsy4KoI330AeMBm+tVOxKaUUqp+0r6RWimlFC48T04ThFJKZQQX7pPTBKGUUsqeJgillMoAWsWklFIqZTRBKKVUJnBhrA1NEEoppWxpglBKKWVLE4RSSilbmiCUUkrZ0gShlFLKliYIpZRStjRBKKVUBtAb5ZRSSqWMJgillFK2NEEopZSypQlCKaUygA73rZRSytaKrQcdn6cmCKWUUrY0QSilVAbIa5nr+Dw1QSillLLlSIIQkXEisl5EikVkqs3nuSLyovX5YhEpCPlsmjV9vYiMjXeeSiml3JV0ghCRbOBx4FKgH3C1iPQLK3YdcMAY0wt4BHjI+m4/YBJwJjAOeEJEsuOcp1JKKRc5cQUxFCg2xmwyxpQDLwATw8pMBGZZr18GRomIWNNfMMacMMZ8CRRb84tnnkoppVzkRILoAmwNeb/NmmZbxhhTCZQC7aN8N555AiAiN4pIkYgUlZSUJPFnKKWUCpX2jdTGmCeNMYXGmMK8vDyvw1FKqYzhRILYDnQNeZ9vTbMtIyI5QGtgX5TvxjNPpZRSLnIiQSwFeotIdxFpTKDReU5YmTnAZOv1lcC7xhhjTZ9k9XLqDvQGlsQ5T6WUUpYsF8b7zkl2BsaYShH5MTAPyAZmGmPWiMh9QJExZg7wNPA3ESkG9hM44GOVewlYC1QCNxljqgDs5plsrEoplanEhSdCJJ0gAIwxc4G5YdPuDnldBlwV4bsPAA/EM0+llFL2xIUriLRvpFZKKeUOTRBKKaVsaYJQSillSxOEUkplABeaIDRBKKVUJhAXWqk1QSillLKlCUIppZQtTRBKKaVsaYJQSillSxOEUkopW44MtaGUUl7JyRLev30kjbOzKK+qplGWsHpHKecUtOPIiUpaNWlEs8bZHDlRSf9733Y1ljM6t+LznYdsP3t6ciE/f2Ule4+Uu7JsN4ba0AShVAP18bSLKa+spvR4Bcu3HqRRdhbT/rXK67AStnT6aNo2b1xr2sWtmgDQskmjmmktmzRiwoDOvLFyp2uxvPiDYazeVsp3nlpcM23FPWNo3TQQR9Gdl9RML5j6hmtxOEUThFINVOfWTWteD8hvQ9Hm/R5GU3/hycErm2dMAKCwoF2t6bk59jX5H0+7mKpqw/kPLXRk+TpYn1LKNcbrAFKgT8eWri8j3gN159ZNyW/bzLnlunAvtSYIpRQApgFkiGE92sUulKa0DUIpBUCL3ByOnKh0bf5TRvRkRN+O7D9aTmFBW5o2ymbV9lK6tmvGeTPerSn3s7F9uWlkLz7YUMKWfce4ckg+a3aUcsUfP457WZseHM/mfUd5dfkOHl2wwY0/R9WTJgjlifsmnkmXNk25blaR16GkVJbApl8F6qrr20g5ZURPbh93OgePlTPovvmOxWZCLiFuH3d6nc+H9WhfZ9pNI3sBcEHvPC7oHZg25LR2/GHSIM7u2pZu7QNVKG+u2snO0jLeXbeHD4v31ppHVpbQI68F/3tJH74x8FRG/+59INA7qbK6AVzWhHnrlgv41dx1nFPQlt++/UWtz5o3zuaKIfk8+/GWlMSiCUKlTP8urfnW4C784t9rOfPUVlRUZe7O37JJDjeP6s22A8cpaN+Me/+9liV3jHKkQbWgfXMA2jSr/7x65DWvM83JX2PioC613l/avzMAk4cX0POOyA+K7JnXnFsv6cMVQ/Lp1KoJ0/61kptH96l11ZKMo+XuXXUFJVvTc/oprZj1/aEA/Pji3lz82/cYe9Yp/Dwkad838SwAqqpN1PWZLE0QKqqHrxpI+xaNOb9XB/YfK6djyyZMfPw/rNh6MOF5/fsn5wPwjYGn0r5FLos37XM4Wn84t0d7/nztEFpZXSyNMVx7bgFZMZ4qf26P9px5aisgcFb95KJNtuVMyKF80c9GcuFvEu8Fc9mAU+vONwX5OjvGOhARfjKqd837X1850NHlbztw3NH5DcxvzYptpVHLJNs28O5tIyJ+Fro+dbhv5ZjZU4az6t4xMcsZYETfjuRkZ9GxZRNHlt2+RW7S85j1/aG8dcsFLL5jlAMRRTemXydaNYn/XKrvKS1rkgMEDnqxkkPL3Bz+fv3XuPOyftx5WT/uGH8Gm2dM4ONpFwPQPsKVR7f2zfjyV+P5dmFXendswYq7Y/+mkZg06Mf0c5uqr0QMOa1tnWkLbxvB9ed3553/vSiheXVqFd92nEjvogt6d0goBrfpFUQD8MqPhnNa+2YU/vIdAJ68ZghndwvsKJ9MG8WwXy2I+F1jd1oZ5VTzj98dzI+e+zSuuFo1bVTr/bcLu3LHhDP4at8xDpdV0KtTCw4eq2DMI4tqyvTv0rrmSiRo84wJ7Dh4nOFRqiG6d2jOvFsu5LXl2/nZyyvrfB/qtgkEpwNUVxum/WsVVxXm0+/UVuwsLWPUw+/H9XfGY9UvxtpO79y6aZ34mjWuvduKCA9dOaDm/cYHx5Ml0H1a7aqHK4fk8/KybZGD8H9+IEaerZfuHZpz52X9AJj/0wt5bfkOHltYbFv2hgu685cPvgTc6Vb6dZsru3j57nkQItJOROaLyAbr/7rpOVBuslVmg4hMDpk+RERWiUixiDwq1l8oIleJyBoRqRaRwmRiVIGzpg4tcvnVt/rzl2sLGXPmKTWfndI68auCrw+MvBFf2r8zy+4czR3ja5/p2e3YZ3RuVev9Q1cOoHXTRvTPb83wXh3o2LIJfTrV7rcerJsNd2qbpkwY0LnWtBnf6l/zeuFtI2ick8VVhV350/8bzJI7RvH05EIWRrh8v3xQ7b8xKytwEC4saEezxjn0zGth+71UmNC/c9TPs7MEEeG687tzfq8OTLeuRq6/oHvU76VBfnClK2eo3p1actvYvhE/nz6hH+dajfWDT2tjWyb0QP2bKwfQOMKNcvZfjr9oKiRbxTQVWGCM6Q0ssN7XIiLtgHuArwFDgXtCEskfgRuA3ta/cdb01cC3gEUox1w9tBuX9OuU9HyuO787D18VuW64fYtc2x4vTmgXpZH3VyEJ4aOpF5MV4Wgy7qzOdGzVhFFndKJ7h5ONtUunj655nczB0u2DWKzqqqC7LuvH36//Gjdc2AMINH5GUx1nI8Rp7Z27uStRbpy12/ng9pF8+PORtp9d2CcPgK7tYq+Hqwq7JrbgJDY8P95JPRGYZb2eBVxuU2YsMN8Ys98YcwCYD4wTkc5AK2PMJyZQj/Fs8PvGmM+NMeuTjE3F6dtRNmK7RkURIbdR3U3n9nEnz7wG5Lep9Vmw14WbQuv9T23TNOEdJq9lbkIHvz9MGsSCWy9iUNc2taZ3apXYVVmshls32C0x3kZqL09y3U6+QV3bNSO/bTOWxGrjSlVAcfBjI3UnY0xw5KtdgN3paRdga8j7bda0Ltbr8OkJEZEbRaRIRIpKSkoS/boicOb96NVn15p2mVVdE+ks3M6UEb1qvQ/9aqyD5ps3XxD3cqJ5+6cX1jRc16dO9tUp59G1XVN+NKJnzLITB3WhZ14LXr3pPG4ZfbLnzeButjWtEXmRIOzEe/L63a+dBsA5BYn9nU5wup79lBjbZcdYyT7Dbz+PmSBE5B0RWW3zb2JoOesqIOVryxjzpDGm0BhTmJeXl+rF88R3B/PyD89N+XKdlJUlfCOsXSFmd8Q4zlcS2XfC2yPqq0+nljXJqD7H3bbNG/PB7RfHrI4Jl0zVhz/SQ/xVTMF2q5gHTxe0a94odiGXdI6zvc4vv6cTYvZiMsaMjvSZiOwWkc7GmJ1WldEem2LbgREh7/OB96zp+WHTt8cRs6+0bJJTZ/TGdDdxUOyeFOEncl3bNbUv6KFUXv2HLivR5fqmliLR0zsPTp4nDuzC++tLeHX5jpQv2zZ/+ubH82EvJmAOEOyVNBl4zabMPGCMiLS1GqfHAPOsqqlDIjLM6r10bYTv+1rj7IZ5K8l5vU721549ZTivTjkvankvdqNUNWgCtGlW/zPbVMYZTbz3QXh5TMzKEq4Ykh+7YJzi+VuCz3Lo0tZ/J0FuS/boNgO4REQ2AKOt94hIoYg8BWCM2Q/cDyy1/t1nTQOYAjwFFAMbgTet739TRLYB5wJviMi8JOOsl9lThnPtuadFLTO0e2ZdPUDgTCknK7BpRNqBWjXJoVOrXH59xQDO7tbWkZvfnJbKA1nLkBvpEl2sFwdcu1QQb5XgyL4dGd6zPT+L0h3UTcN7doj4jAU3XH9+oHvw18L3dReuoJK5WdGNzSipG+WMMfuAOs38xpgi4PqQ9zOBmRHK1eneYoyZDcxOJjY3LLj1olo3R/1h0iBXLuucFNqNM5a7LuvH/a+vxQB3TjiDNs0aMT5Cn3sRYfEdEWsfbcrHXdQxfU9xf+z/ZPTr3Iq1Ow/55u7ZeMfFa56bwz9uGOZuMFFkZwn/mXoxhb98JyXtf+HdimttyzZZ1eeHhITondRRhN/gEnpz1Gntm9UZkMyP6pz1RNGhReAeA2MMbZs35i7r7tJ01TaJwewSVZ9qokHd2lgJIvWdK+zY3jXvUx1a5Na6013hyD1O4RpmBXocvje8gDNPbR3x81NbZ159ZHB00ET78ccyqGsb126ci0dey9RWf8V7Bum343EDHFmbnaVlXofgmERqC+KlVxAR3PuNM4HIO3F9hqjwgt2wzpFc2LsDf5g0iHFnnRK7cAJevSl6A7ZbgsfpVByI61etYJL4rhv8myGmjz+Dz3cd8jqMuvzz47lS3a0JIkS3ds34av+xWtPSYYTLaEJ7G8UiImlRbRY3j/bdRG4uBG96MSVzJ7UXgsOFeCFq1ZttG0Qy98T4J+GAVjHVYjcUsKq/J7472Ce9vFJ75GsUZ9dnvx2QfRZOSjSxGTImEtvjvsNn7RnViynTJNJI568870/j+3eO2AsqFbw6Gws+9CdeXtRS2I2l5beElQqtm8a+fyXaesn044BeQcTQEHcalbjQaoV4qxi83LbsOg3EO9SGk7wYz6m+/Fb9E86Po7lmlIR2D39vKyqE35O8N3eZ1+XFagofA8xtTRplOzq/eIb8TmeaIELYDRgXvtNMOicwNHYHH945bMfvZz1uCp5RpeLAlwlr2Yv7IFK9xPAHPcU16GSUz660hv04P4HOIOEKHWr71CsIlwQTQzw/coHV1zgTDgiZLpW/UX12zp9fejrfLuzK5Wf7o+eY36+0vGb3G0uUz+Ll5V3psWiCIDMP9rHGuW9IUnlmnMizHdo1b8xDVw5wvNojnWhSqjtiQ325UVugCYLo2d+pDThYNQWJ93KpD7teKg1NKsfJCu6cY890friDVEn3e37cksoklsyytIrJZU5tCEunj2ZYj0D//+BAbD8ZdfKJYxf1cXfsnUvPOoWmGXhWmsoRPP2oW7tm3OjiDWPV1a7Nuhan6txTzS8N++E6tQq0h/rxeRAZIdhXv1NrZxqe81rm1hygvze8gNW/GEuXNifHbrrhAnfvCu3m4UPl/SiVO7GbZ5uLbh/JHePPcGRedgeTVK2n0EWf3a1NipaauYIPLNMb5VwyZURPJg8voEWu3eqovdvEewAY0bcjC9eXUNChec1837rlAjq0yKVtc/dHGQ3G6aOhYlLO743UfpOqtprQxQzIb5OSZUYSz+/WkKve9AqCwNmUfXKIkhBibFjXnnsan951Sa1udaef0ipm91i7IYy/+7VuLL5jFKdGGCDwD5MG1XofWr2UCQeuZKXiuNescWCdt0nhEONOa7iHwThZO9Oo0zsCuDJKQL32Vxd/OE0QLhER2jl4pdCpVZOIdYwTB3VhzS/G1rz/4UU9HVtuOqu5DyIFGeKiPnncP/FM7pzgTBWQJzRD2Aom/+CJV+9OLdk8YwIDu7Yh2Gkt3vG33OTGyaBWMcXg1rGlR4fmbNp71LH5NQ+5AsrUbpOJ7gCpvElQRLjm3IKULc8NXgy1kQ6+N7w7FVWG759fUOez4T07cOOFPbj+gu6OLMtvP4H3aa+B0pFjVUOVbtWejXOyuGlkL3Jz6p54ZWcJd4w/g44tvbvvyM02Ek0QHvnlN+s8ipu5/3MBQMwuqpHaS5Q9n52U+Vaiz7FQ/qI3ynnAreycm5PNDWGXpf2sG+hiLTNaPfdZXeKbRzoJPhVvaPcEH1uqx7uEnNUl8iN2neSn8cH8E4k/JZUgRKSdiMwXkQ3W/7b1JiIy2SqzQUQmh0wfIiKrRKRYRB4VqxVWRH4jIutEZKWIzBaRNsnE6aRUHHgfuLw/HVrkctmAQC+J0WfUvjv3/N4d2DxjAhMGdOaaYafVTJ//0wt5PmxcFz/tjPU1pFtgs5rQ39lHoTZkdg33egHhPb/9BsnWVUwFFhhjZojIVOv9z0MLiEg74B6gkMDV/jIRmWOMOQD8EbgBWAzMBcYBbwLzgWnGmEoReQiYFj5frzl54L19XF+uP//kzXNXDMnnCmuUyEcnGbIijO/z+HcG13rfu1NLx2Lyo0TXec3OljkXU65KWQOpjw6CumlEl2wV00RglvV6FnC5TZmxwHxjzH4rKcwHxolIZ6CVMeYTEzideTb4fWPM28aYSuv7nwD5ScZZb6nYabJFIg7YFSk5NCT1/Qk0P0Rmfye1rilVW7IJopMxZqf1ehdgN1JZF2BryPtt1rQu1uvw6eG+T+CqwpaI3CgiRSJSVFJSkkjscXFzl0nlYHIZQVeXq/zWxbIh8ttvELOKSUTeAewqf6eHvjHGGBFx9M8TkelAJfBcpDLGmCeBJwEKCwsdX71++8FU4rx4EI5SmSBmgjDGjI70mYjsFpHOxpidVpXRHpti24ERIe/zgfes6flh07eHzPt7wGXAKKN7eMJ0jekVWqKcei6ByhzJbhFzgGCvpMnAazZl5gFjRKSt1ctpDDDPqpo6JCLDrN5L1wa/LyLjgNuBbxhjjiUZY1LSvV5Wj5HaBhGvPp1a8pOLe/HB7SO9DqXBqs/+6ubJYLIJYgZwiYhsAEZb7xGRQhF5CsAYsx+4H1hq/bvPmgYwBXgKKAY2crKt4TGgJTBfRJaLyJ+SjNMxemaePmoaqfU3i9utY/rStZ0OF5+OfDcWkzFmHzDKZnoRcH3I+5nAzAjl6txSbIzplUxcqaBn5v6nv1FkWmvrvlgjN6cDHbMhhl4dW8QupHwt3asJ3eDlGvFT3nbryYszv1fIGZ0Tf7Sw3/K2JogYBlt38Q61ntqkUq++O00m3EWeifx0ZdfPpefDX3x6+j6bPJR2W4iXCxt1cJz5eIfnPr9X4PnW8QzW57MTEUf46LiiVIOgVxAxuHnJ98OLetIoO4vvfK1bXOXvv/wsbhrZK6GnlulB1X+X7X7QkLeL5XdfwqD75nsdhi0/XV2BJoi4ufG7NWmUzU0j42+Pb5yTRbf22sMkXn7b2ZQ/pPNjYVNNq5hUxtMLCKXqRxNEDJF6wOjJaRrRDFGHt72YdO9xkp9vlGswtLoi/ehv5k/a7TiyZA72bmzumiAy1JXW8yTyWqb/zToqs2ingfShjdSxpOnG/IMLe3D9+d3JydZzAD1j9Re9sousXmMxubh9a4KIU7rVm4oIOdnpFbPT0u03ayj0d3GHG4lXTy8T1Ng6I8/NcecWfaWU8gu9goihRZPAKjq1TVMArjn3NEqPV/CDi3pE+5pyUH0voXOsx7XePKq3k+GoDKKVj9FpgohhQH4bHv/OYEaengcEbm67bWzfOuU+mTaKYb9akOrwGpREHwCUlSVsnjHBpWiUcp7fGvA1QcRhwoDOMcuc0rpJCiJRyhl+OxApf9I2CKUaJO8yhPZiiizTniinlFLKF5zPvJoglFJK2dIEoZRSypYmiDA/Hd2HU1ppg7PKbF42UmdnaSNEutBeTGFuHt2bm0drv3ml3JKjCSJtJHUFISLtRGS+iGyw/m8bodxkq8wGEZkcMn2IiKwSkWIReVSsju4icr+IrBSR5SLytoicmkycSimlEpdsFdNUYIExpjewwHpfi4i0A+4BvgYMBe4JSSR/BG4Aelv/xlnTf2OMGWCMGQS8DtydZJwqnWmffaU8kWyCmAjMsl7PAi63KTMWmG+M2W+MOQDMB8aJSGeglTHmE2OMAZ4Nft8Ycyjk+83RQ4RCH9LkJN2hVDySbYPoZIzZab3eBXSyKdMF2Bryfps1rYv1Onw6ACLyAHAtUAqMjBSAiNwI3AjQrVu3xP8CpVRKJTpkiorOzWQf8wpCRN4RkdU2/yaGlrOuAhyL1Rgz3RjTFXgO+HGUck8aYwqNMYV5eXlOLV4ppdKKG3k35hWEMWZ0pM9EZLeIdDbG7LSqjPbYFNsOjAh5nw+8Z03PD5u+3eb7zwFzCbRjKKUcoGMxqXgk2wYxBwj2SpoMvGZTZh4wRkTaWo3TY4B5VtXUIREZZvVeujb4fREJ7Wc6EViXZJxKKaUSlGwbxAzgJRG5DtgC/BeAiBQCPzTGXG+M2S8i9wNLre/cZ4zZb72eAjwDNAXetP4BzBCRvkC1Nd8fJhmnUkqpBCWVIIwx+4BRNtOLgOtD3s8EZkYod5bN9CuSiUspFZ2X7cTaRJ0+dKgNpRqQztZzS5roI3NVHDRBKNWAnNuzPXDyUboN1a+vGOB1CGmhYW8lKi00zgmcx+Rka+VEsh78Zn+uGtKV7h2aexZDbiPvz0vbNGsEQLtmjT2OpLasetT9BfePbBfqDTVBOOipawup0v6DCXnk2wPpFGP03GmXnkHrZo2Y0D/2o19VdE0aZddcRXjll5f3p6B9c24dU/fZ7qkyom9Hvn9ed354UQ/PYgg1e8pwvvnER1wxOD924TD3TzyLbu2aMfL0jo7HJSaDDmiFhYWmqKjI6zCUUiqtiMgyY0xh+HTvr/WUUkr5kiYIpZRStjRBKKWUsqUJQimllC1NEEoppWxpglBKKWVLE4RSSilbmiCUUkrZyqgb5USkhMDw4PXRAdjrYDhuSZc4IX1i1Tidly6xapwBpxlj6jySM6MSRDJEpMjuTkK/SZc4IX1i1Tidly6xapzRaRWTUkopW5oglFJK2dIEcdKTXgcQp3SJE9InVo3TeekSq8YZhbZBKKWUsqVXEEoppWxpglBKKWVLEwQgIuNEZL2IFIvIVI9i2Cwiq0RkuYgUWdPaich8Edlg/d/Wmi4i8qgV70oRGRwyn8lW+Q0iMtmBuGaKyB4RWR0yzbG4RGSI9XcXW9+t13MTI8R5r4hst9bpchEZH/LZNGuZ60VkbMh0221BRLqLyGJr+osiUu9nVYpIVxFZKCJrRWSNiNxsTffVeo0Sp6/Wq4g0EZElIrLCivMX0eYtIrnW+2Lr84L6xu9QnM+IyJch63OQNd2z/amGMaZB/wOygY1AD6AxsALo50Ecm4EOYdN+DUy1Xk8FHrJejwfeBAQYBiy2prcDNln/t7Vet00yrguBwcBqN+ICllhlxfrupQ7GeS9wm03ZftbvnAt0t37/7GjbAvASMMl6/SfgR0ms087AYOt1S+ALKyZfrdcocfpqvVp/YwvrdSNgsfW3284bmAL8yXo9CXixvvE7FOczwJU25T3bn4L/9AoChgLFxphNxphy4AVgoscxBU0EZlmvZwGXh0x/1gR8ArQRkc7AWGC+MWa/MeYAMB8Yl0wAxphFwH434rI+a2WM+cQEtu5nQ+blRJyRTAReMMacMMZ8CRQT2A5stwXrLOxi4GWbv7k+se40xnxqvT4MfA50wWfrNUqckXiyXq31csR628j6Z6LMO3Q9vwyMsmJJKH4H44zEs/0pSBNEYIPfGvJ+G9F3ArcY4G0RWSYiN1rTOhljdlqvdwGdrNeRYk7V3+JUXF2s127G+2Pr8nxmsMqmHnG2Bw4aYyqdjtOq3jibwNmkb9drWJzgs/UqItkishzYQ+CAuTHKvGvisT4vtWJxfb8Kj9MYE1yfD1jr8xERyQ2PM854HP/dNUH4x/nGmMHApcBNInJh6IfWGYHv+iT7NS7LH4GewCBgJ/Cwp9GEEZEWwCvALcaYQ6Gf+Wm92sTpu/VqjKkyxgwC8gmc8Z/ubUT2wuMUkbOAaQTiPYdAtdHPvYuwNk0QsB3oGvI+35qWUsaY7db/e4DZBDby3dZlI9b/e6zikWJO1d/iVFzbrdeuxGuM2W3tkNXAXwis0/rEuY/A5X2OU3GKSCMCB93njDH/sib7br3axenn9WqMOQgsBM6NMu+aeKzPW1uxpGy/ColznFWVZ4wxJ4C/Uv/16fz+lEwDRib8A3IINPJ052QD1JkpjqE50DLk9UcE2g5+Q+1Gy19brydQu/FqiTnZePUlgYarttbrdg7EV0Dtxl/H4qJuo9p4B+PsHPL6pwTqlwHOpHZj5CYCDZERtwXgn9Ru8JySRJxCoH7492HTfbVeo8Tpq/UK5AFtrNdNgQ+AyyLNG7iJ2o3UL9U3fofi7Byyvn8PzPDD/mSM0QRhrdTxBHpobASme7D8HtZGtwJYE4yBQL3oAmAD8E7IRiDA41a8q4DCkHl9n0DjWjHw3w7E9jyBaoQKAnWa1zkZF1AIrLa+8xjW3f0Oxfk3K46VwBxqH9imW8tcT0hPj0jbgvUbLbHi/yeQm8Q6PZ9A9dFKYLn1b7zf1muUOH21XoEBwGdWPKuBu6PNG2hivS+2Pu9R3/gdivNda32uBv7OyZ5Onu1PwX861IZSSilb2gahlFLKliYIpZRStjRBKKWUsqUJQimllC1NEEoppWxpglBKKWVLE4RSSilb/x/WTNwQM4yF2gAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.step(x, y_stages)\n",
    "plt.plot(x,y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-19T10:22:13.002763Z",
     "end_time": "2023-07-19T10:22:20.703907Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file with 1798 windows\n"
     ]
    },
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_windows = load_data_one_file(data)\n",
    "len(stages) == len(data_windows)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-19T10:22:20.743682Z",
     "end_time": "2023-07-19T10:22:24.783206Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scales = range(1, 248)\n",
    "waveletname = 'morl'\n",
    "coefs_sq = []\n",
    "for stage in [0,1,2]:\n",
    "    random_ind = np.random.choice(np.where(stages == stage)[0])\n",
    "    random_data = data_windows[random_ind]\n",
    "    coef, freq = pywt.cwt(random_data, scales, waveletname)\n",
    "    coef2 = np.square(coef)\n",
    "    coefs_sq.append(coef2)\n",
    "avgs = [np.average(coef, axis=1) for coef in coefs_sq]\n",
    "\n",
    "def plot_coefs(coef):\n",
    "    plt.imshow(coef, extent=[-1, 1, 1, 128], cmap='PRGn', aspect='auto',\n",
    "           vmax=abs(coef).max(), vmin=-abs(coef).max(), label=stage)\n",
    "    plt.title(stages_names_3_outputs[stage])\n",
    "    plt.show()\n",
    "\n",
    "def plot_mult_lines(avgs):\n",
    "    for avg in avgs:\n",
    "        plt.plot(avg)\n",
    "    plt.legend(stages_names_3_outputs.keys())\n",
    "    plt.show()\n",
    "#plot_coefs(coefs)\n",
    "plot_mult_lines(avgs)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-19T10:22:24.824106Z",
     "end_time": "2023-07-19T10:22:28.102759Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<RawEDF | CX230050.edf, 1 x 17981000 (35962.0 s), ~7 kB, data not loaded>",
      "text/html": "\n\n<table class=\"table table-hover\">\n    <tr>\n        <th>Measurement date</th>\n        <td>May 30, 2006  21:44:47 GMT</td>\n        \n    </tr>\n    <tr>\n        <th>Experimenter</th>\n<td>Unknown</td>\n    </tr>\n        <th>Participant</th>\n<td>Unknown</td>\n    </tr>\n    <tr>\n        <th>Digitized points</th>\n        <td>Not available</td>\n    </tr>\n    <tr>\n        <th>Good channels</th>\n        <td>0 magnetometer, 0 gradiometer,\n            and 1 EEG channels</td>\n    </tr>\n    <tr>\n        <th>Bad channels</th>\n        <td></td>\n        \n    </tr>\n    <tr>\n        <th>EOG channels</th>\n        <td>Not available</td>\n    </tr>\n    <tr>\n        <th>ECG channels</th>\n        <td>Not available</td>\n    <tr>\n        <th>Sampling frequency</th>\n        <td>500.00 Hz</td>\n    </tr>\n    <tr>\n        <th>Highpass</th>\n        <td>0.00 Hz</td>\n    </tr>\n     <tr>\n        <th>Lowpass</th>\n        <td>250.00 Hz</td>\n    </tr>\n\n    <tr>\n        <th>Filenames</th>\n        <td>CX230050.edf</td>\n    </tr>\n    <tr>\n        <th>Duration</th>\n        <td>09:59:21 (HH:MM:SS)</td>\n    </tr>\n</table>\n"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-19T10:22:28.125085Z",
     "end_time": "2023-07-19T10:22:28.282174Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# go trough windows of length 3\n",
    "# if not all in the window are in the same stage then -1\n",
    "windows = np.empty(len(stages)-2)\n",
    "for i in range(len(stages)-2):\n",
    "    windows[i] =  stages[i] if stages[i] == stages[i+1] == stages[i+2] else -1\n",
    "\n",
    "# choose at random a window for all three stages\n",
    "def draw_window_3(stage):\n",
    "    # choose a random\n",
    "    ind = np.random.choice(np.where(windows == stage)[0])\n",
    "    slices = data_windows[ind:ind+3]\n",
    "    coefs = [pywt.cwt(s, scales, waveletname)[0] for s in slices]\n",
    "    coefs_sq = [np.square(coef) for coef in coefs]\n",
    "    avgs = [np.average(coef, axis=1) for coef in coefs_sq]\n",
    "    plot_mult_lines(avgs)\n",
    "draw_window_3(0)\n",
    "draw_window_3(1)\n",
    "draw_window_3(2)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-19T10:22:28.293112Z",
     "end_time": "2023-07-19T10:22:38.195283Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "len(np.where(windows == 0)[0])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-18T20:05:40.378407Z",
     "end_time": "2023-07-18T20:05:40.408386Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "avgs = []\n",
    "coefs = []\n",
    "for stage in [0]:\n",
    "    while True:\n",
    "        random_ind = np.random.randint(len(stages))\n",
    "        if stages[random_ind-1] == stages[random_ind] == stages[random_ind+1]:\n",
    "            break\n",
    "    # random_data = data[random_ind]\n",
    "    for i in [-1,0,1]:\n",
    "        coef, freq = pywt.cwt(data_windows[random_ind+i], scales, waveletname)\n",
    "    coefs.append(coef)\n",
    "avgs = [np.average(coef, axis=1) for coef in coefs]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-13T12:18:13.207694Z",
     "end_time": "2023-07-13T12:18:15.837512Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "len(avgs)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-13T12:18:35.403358Z",
     "end_time": "2023-07-13T12:18:35.418098Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "data_windows.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-13T12:13:06.507127Z",
     "end_time": "2023-07-13T12:13:06.553991Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "np.random.choice(data)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-13T11:47:14.275553Z",
     "end_time": "2023-07-13T11:47:14.294416Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "for coef in coefs:\n",
    "    plot_coefs(coef)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-13T11:40:07.571686Z",
     "end_time": "2023-07-13T11:40:08.637002Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-13T11:31:02.059295Z",
     "end_time": "2023-07-13T11:31:02.095013Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
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    "plt.plot(avgs[0])\n",
    "plt.plot(avgs[1])\n",
    "plt.plot(avgs[2])\n"
   ],
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     "end_time": "2023-07-13T11:31:59.694454Z"
    }
   }
  }
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